App.py file

app.py

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+# -*- coding: utf-8 -*-
+"""usemodel.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1c8Qtf9TWr3apElEv2uDgCD_MQwHnmw0B
+"""
+
+import pickle
+from mygrad import Neuron, Value
+import streamlit as st 
+def convertToOneHotEncode(tags):
+    tag1 = tags[0]
+    tag2 = tags[1]
+    vec1 = [0]*5
+    vec2 = [0]*4
+    vec1[tag1] = 1
+    vec2[tag2-1] = 1
+    vec1.extend(vec2)
+
+
+
+    return vec1
+def loadModel():
+    neuron1weightsbias = []
+    with open(f'weights.pkl', 'rb') as file:
+        neuron1weightsbias = pickle.load(file)
+    neuron = Neuron(10)
+
+    neuron.w = [Value(i) for i in neuron1weightsbias[:-1]]
+    neuron.b = Value(neuron1weightsbias[-1])
+    return neuron
+
+import json
+def loadjson(filepath):
+    data = []
+    with open(filepath, 'rb') as file:
+        for line in file:
+            data.append(json.loads(line))
+    return data
+
+data = loadjson('data/train.jsonl')
+data2 = loadjson('data/test.jsonl')
+X = [element['pos_tags'] for element in data] + [element['pos_tags'] for element in data2]
+Y = [element['chunk_tags'] for element in data] + [element['chunk_tags'] for element in data2]
+
+n = loadModel()
+
+def predictsentence(postagsOfSentence):
+    if postagsOfSentence:
+        postagsOfSentence = [0] + postagsOfSentence
+    else:
+        return
+    xnew = []
+    for ix in range(1, len(postagsOfSentence)):
+        xnew.append([ postagsOfSentence[ix-1], postagsOfSentence[ix]])
+    for ix, pair in enumerate(xnew):
+        xnew[ix] = convertToOneHotEncode(pair)
+    w = Value(0)
+    chunks = []
+    for ix2, wordpair in enumerate(xnew):
+        xinput = [w] + wordpair
+        w = n(xinput)
+        if w.data > 0.5:
+            chunks.append(1)
+        else:
+            chunks.append(0)
+    return chunks
+def input_(input):
+    input = input.split(',')
+    inputs = [int(i) for i in input]
+    return inputs
+st.title('Chunck tagging')
+input = st.text_input('Input the pos tags')
+inputs = input_(input)
+output = predictsentence(inputs)
+st.write(output)
+
+# import pandas as pd
+# data = output
+# df = pd.DataFrame.from_dict(data)
+# st.dataframe(df)
+